- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
前言
yolov5网络结构比较复杂，上次我们简要介绍了yolov5网络模块，并且复现了C3模块，深度学习基础–yolov5网络结构简介，C3模块构建;- 这一次我们将复现backbone模块，将目标检测网络结构用到目标识别上，会是怎样的效果呢？？？；
- 这周是考试周，周一到周四一直都在准备考试和去考试，昨天开始又发高烧，更新较慢；
- 欢迎收藏加关注，本人将会持续更新。
文章目录
- 案例
- 1、数据处理
- 1、导入库
- 2、查看数据类别
- 3、导入数据
- 4、数据集划分
- 5、展示一批数据
- 2、模型构建
- 3、模型训练
- 1、构建训练集
- 2、构建测试集
- 3、设置超参数
- 4、模型正式训练
- 5、结果显示和评估
- 1、结果显示
- 2、评估
案例
将backbone模块用于识别天气分类
1、数据处理
1、导入库
import torch
import torchvision
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import datasets, transforms
device = "cuda" if torch.cuda.is_available() else "cpu"
device
'cuda'
2、查看数据类别
import os, pathlib
data_dir = './data/'
data_dir = pathlib.Path(data_dir)
classnames = [str(path).split("\\")[0] for path in os.listdir(data_dir)]
classnames
['cloudy', 'rain', 'shine', 'sunrise']
3、导入数据
data_transforms = transforms.Compose([
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize( # 数据标准化处理---> 转化为 标准状态分布，使模型更容易收敛
mean=[0.485, 0.456, 0.406], # rgb，均值
std=[0.229, 0.224, 0.225] # rgb，标准差，这两个从数据集中随机抽样得到的
)
])
total_data = datasets.ImageFolder("./data/", data_transforms)
total_data
Dataset ImageFolder
Number of datapoints: 1125
Root location: ./data/
StandardTransform
Transform: Compose(
Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)
4、数据集划分
训练集 ：测试集 = 8 ：2
train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_size
train_data, test_data = torch.utils.data.random_split(total_data, [train_size, test_size])
print("train_size", len(train_data))
print("test_size", len(test_data))
train_size 900
test_size 225
# 动态加载数据集
batch_size = 4
train_dl = torch.utils.data.DataLoader(
train_data,
batch_size=batch_size,
shuffle=True
)
test_dl = torch.utils.data.DataLoader(
test_data,
batch_size=batch_size,
shuffle=True
)
# 查看数据格式
temp_data, temp_label = next(iter(train_dl))
print("data: ", temp_data.shape)
print("data_labels: ", temp_label)
data: torch.Size([4, 3, 224, 224])
data_labels: tensor([3, 2, 0, 0])
5、展示一批数据
这里一批次大小：4
import matplotlib.pyplot as plt
temp_images, temp_labels = next(iter(test_dl))
plt.figure(figsize=(20, 10))
for i in range(4):
plt.subplot(5, 5, i + 1)
plt.imshow(temp_images[i].cpu().numpy().transpose(1, 2, 0)) # （C， H， W) ==> （H， W， C）
plt.title(classnames[temp_labels[i]])
plt.axis('off')
plt.show()
2、模型构建
整体网络：
C3网络参考：深度学习基础–yolov5网络结构简介，C3模块构建
SPPF网络模块图结构如下：
import torch.nn.functional as F
import warnings # 确保导入 warnings 模块
# 自动计算p(填充)
def autop(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [i // 2 for i in k]
return p
# Conv模块搭建
'''
卷积层 + 标准化 + 激活函数
'''
class Conv(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
super().__init__()
'''
groups:
1: 标准卷积
c1: 深度卷积
1 ~ c1: 分组卷积
bias:
false: 不使用偏置
'''
self.conv = nn.Conv2d(c1, c2, kernel_size=k, stride=s, padding=autop(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
'''
act:
true: silu激活函数
否则: 如果是nn.Mudule(如: nn.Relu), 则调用本身
否则: Identity, 什么都不做
'''
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module()) else nn.Identity())
def forward(self,x):
return self.act(self.bn(self.conv(x)))
# Bottleneck模块, 用于特征提取和用于防止梯度消失、梯度爆炸问题
class Bottleneck(nn.Module):
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # shortcut: 是否需要残差连接， e: 模型深度
super().__init__()
c_ = int(c1 * 2)
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
# 搭建C3模块
class C3(nn.Module):
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__()
'''
刚开始: 卷积层2层
后面: n层bottlenck
后 concat
后 conv
'''
c_ = int(c1 * e)
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1) # 用于拼接
self.cv3 = Conv(2 * c_, c2, 1)
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) # * 解包
def forward(self, x):
# 拼接，按照 dim=1维进行拼接，故列要相同
return self.cv3(torch.cat([self.m(self.cv1(x)), self.cv2(x)], dim=1)) # 结合图就知道了结构
# 搭建SPPF模块，用于特征融合
class SPPF(nn.Module):
def __init__(self, c1, c2, k=5):
super().__init__()
c_ = c1 // 2
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * 4, c2, 1, 1) # 模型融合，这个时候模型通道扩大4倍，套用池化层公式，发现通过.m 通道数数不变
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) # 套用卷积层、池化层公式，发现输出通道不变
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
y1 = self.m(x)
y2 = self.m(y1)
y3 = self.m(y2)
return self.cv2(torch.cat([x, y1, y2, y3], 1)) # 结合图
# 搭建backbone模块
class Yolov5_backbone(nn.Module):
def __init__(self):
super(Yolov5_backbone, self).__init__()
# 采用常规卷积, kernel_size, stride 与 yolov5.yaml配置文件一致
self.conv_1 = Conv(3, 64, 3, 2, 2)
self.conv_2 = Conv(64, 128, 3, 2)
self.c3_3 = C3(128, 128)
self.conv_4 = Conv(128, 256, 3, 2)
self.c3_5 = C3(256, 256)
self.conv_6 = Conv(256, 512, 3, 2)
self.c3_7 = C3(512, 512)
self.conv_8 = Conv(512, 1024, 3, 2)
self.c3_9 = C3(1024, 1024)
self.SPPF_10 = SPPF(1024, 1024, 5)
self.classifiler = nn.Sequential(
nn.Linear(in_features=65536, out_features=100),
nn.ReLU(),
nn.Linear(in_features=100, out_features=len(classnames))
)
def forward(self, x):
x = self.conv_1(x)
x = self.conv_2(x)
x = self.c3_3(x)
x = self.conv_4(x)
x = self.c3_5(x)
x = self.conv_6(x)
x = self.c3_7(x)
x = self.conv_8(x)
x = self.c3_9(x)
x = self.SPPF_10(x)
x = torch.flatten(x, start_dim=1)
x = self.classifiler(x)
return x
# 输出参数
model = Yolov5_backbone().to(device)
model
Yolov5_backbone(
(conv_1): Conv(
(conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv_2): Conv(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(c3_3): C3(
(cv1): Conv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(conv_4): Conv(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(c3_5): C3(
(cv1): Conv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(conv_6): Conv(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(c3_7): C3(
(cv1): Conv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(conv_8): Conv(
(conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(c3_9): C3(
(cv1): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(SPPF_10): SPPF(
(cv1): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
)
(classifiler): Sequential(
(0): Linear(in_features=65536, out_features=100, bias=True)
(1): ReLU()
(2): Linear(in_features=100, out_features=4, bias=True)
)
)
import torchsummary as summary
summary.summary(model, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 113, 113] 1,728
BatchNorm2d-2 [-1, 64, 113, 113] 128
SiLU-3 [-1, 64, 113, 113] 0
Conv-4 [-1, 64, 113, 113] 0
Conv2d-5 [-1, 128, 57, 57] 73,728
BatchNorm2d-6 [-1, 128, 57, 57] 256
SiLU-7 [-1, 128, 57, 57] 0
Conv-8 [-1, 128, 57, 57] 0
Conv2d-9 [-1, 64, 57, 57] 8,192
BatchNorm2d-10 [-1, 64, 57, 57] 128
SiLU-11 [-1, 64, 57, 57] 0
Conv-12 [-1, 64, 57, 57] 0
Conv2d-13 [-1, 128, 57, 57] 8,192
BatchNorm2d-14 [-1, 128, 57, 57] 256
SiLU-15 [-1, 128, 57, 57] 0
Conv-16 [-1, 128, 57, 57] 0
Conv2d-17 [-1, 64, 57, 57] 73,728
BatchNorm2d-18 [-1, 64, 57, 57] 128
SiLU-19 [-1, 64, 57, 57] 0
Conv-20 [-1, 64, 57, 57] 0
Bottleneck-21 [-1, 64, 57, 57] 0
Conv2d-22 [-1, 64, 57, 57] 8,192
BatchNorm2d-23 [-1, 64, 57, 57] 128
SiLU-24 [-1, 64, 57, 57] 0
Conv-25 [-1, 64, 57, 57] 0
Conv2d-26 [-1, 128, 57, 57] 16,384
BatchNorm2d-27 [-1, 128, 57, 57] 256
SiLU-28 [-1, 128, 57, 57] 0
Conv-29 [-1, 128, 57, 57] 0
C3-30 [-1, 128, 57, 57] 0
Conv2d-31 [-1, 256, 29, 29] 294,912
BatchNorm2d-32 [-1, 256, 29, 29] 512
SiLU-33 [-1, 256, 29, 29] 0
Conv-34 [-1, 256, 29, 29] 0
Conv2d-35 [-1, 128, 29, 29] 32,768
BatchNorm2d-36 [-1, 128, 29, 29] 256
SiLU-37 [-1, 128, 29, 29] 0
Conv-38 [-1, 128, 29, 29] 0
Conv2d-39 [-1, 256, 29, 29] 32,768
BatchNorm2d-40 [-1, 256, 29, 29] 512
SiLU-41 [-1, 256, 29, 29] 0
Conv-42 [-1, 256, 29, 29] 0
Conv2d-43 [-1, 128, 29, 29] 294,912
BatchNorm2d-44 [-1, 128, 29, 29] 256
SiLU-45 [-1, 128, 29, 29] 0
Conv-46 [-1, 128, 29, 29] 0
Bottleneck-47 [-1, 128, 29, 29] 0
Conv2d-48 [-1, 128, 29, 29] 32,768
BatchNorm2d-49 [-1, 128, 29, 29] 256
SiLU-50 [-1, 128, 29, 29] 0
Conv-51 [-1, 128, 29, 29] 0
Conv2d-52 [-1, 256, 29, 29] 65,536
BatchNorm2d-53 [-1, 256, 29, 29] 512
SiLU-54 [-1, 256, 29, 29] 0
Conv-55 [-1, 256, 29, 29] 0
C3-56 [-1, 256, 29, 29] 0
Conv2d-57 [-1, 512, 15, 15] 1,179,648
BatchNorm2d-58 [-1, 512, 15, 15] 1,024
SiLU-59 [-1, 512, 15, 15] 0
Conv-60 [-1, 512, 15, 15] 0
Conv2d-61 [-1, 256, 15, 15] 131,072
BatchNorm2d-62 [-1, 256, 15, 15] 512
SiLU-63 [-1, 256, 15, 15] 0
Conv-64 [-1, 256, 15, 15] 0
Conv2d-65 [-1, 512, 15, 15] 131,072
BatchNorm2d-66 [-1, 512, 15, 15] 1,024
SiLU-67 [-1, 512, 15, 15] 0
Conv-68 [-1, 512, 15, 15] 0
Conv2d-69 [-1, 256, 15, 15] 1,179,648
BatchNorm2d-70 [-1, 256, 15, 15] 512
SiLU-71 [-1, 256, 15, 15] 0
Conv-72 [-1, 256, 15, 15] 0
Bottleneck-73 [-1, 256, 15, 15] 0
Conv2d-74 [-1, 256, 15, 15] 131,072
BatchNorm2d-75 [-1, 256, 15, 15] 512
SiLU-76 [-1, 256, 15, 15] 0
Conv-77 [-1, 256, 15, 15] 0
Conv2d-78 [-1, 512, 15, 15] 262,144
BatchNorm2d-79 [-1, 512, 15, 15] 1,024
SiLU-80 [-1, 512, 15, 15] 0
Conv-81 [-1, 512, 15, 15] 0
C3-82 [-1, 512, 15, 15] 0
Conv2d-83 [-1, 1024, 8, 8] 4,718,592
BatchNorm2d-84 [-1, 1024, 8, 8] 2,048
SiLU-85 [-1, 1024, 8, 8] 0
Conv-86 [-1, 1024, 8, 8] 0
Conv2d-87 [-1, 512, 8, 8] 524,288
BatchNorm2d-88 [-1, 512, 8, 8] 1,024
SiLU-89 [-1, 512, 8, 8] 0
Conv-90 [-1, 512, 8, 8] 0
Conv2d-91 [-1, 1024, 8, 8] 524,288
BatchNorm2d-92 [-1, 1024, 8, 8] 2,048
SiLU-93 [-1, 1024, 8, 8] 0
Conv-94 [-1, 1024, 8, 8] 0
Conv2d-95 [-1, 512, 8, 8] 4,718,592
BatchNorm2d-96 [-1, 512, 8, 8] 1,024
SiLU-97 [-1, 512, 8, 8] 0
Conv-98 [-1, 512, 8, 8] 0
Bottleneck-99 [-1, 512, 8, 8] 0
Conv2d-100 [-1, 512, 8, 8] 524,288
BatchNorm2d-101 [-1, 512, 8, 8] 1,024
SiLU-102 [-1, 512, 8, 8] 0
Conv-103 [-1, 512, 8, 8] 0
Conv2d-104 [-1, 1024, 8, 8] 1,048,576
BatchNorm2d-105 [-1, 1024, 8, 8] 2,048
SiLU-106 [-1, 1024, 8, 8] 0
Conv-107 [-1, 1024, 8, 8] 0
C3-108 [-1, 1024, 8, 8] 0
Conv2d-109 [-1, 512, 8, 8] 524,288
BatchNorm2d-110 [-1, 512, 8, 8] 1,024
SiLU-111 [-1, 512, 8, 8] 0
Conv-112 [-1, 512, 8, 8] 0
MaxPool2d-113 [-1, 512, 8, 8] 0
MaxPool2d-114 [-1, 512, 8, 8] 0
MaxPool2d-115 [-1, 512, 8, 8] 0
Conv2d-116 [-1, 1024, 8, 8] 2,097,152
BatchNorm2d-117 [-1, 1024, 8, 8] 2,048
SiLU-118 [-1, 1024, 8, 8] 0
Conv-119 [-1, 1024, 8, 8] 0
SPPF-120 [-1, 1024, 8, 8] 0
Linear-121 [-1, 100] 6,553,700
ReLU-122 [-1, 100] 0
Linear-123 [-1, 4] 404
================================================================
Total params: 25,213,112
Trainable params: 25,213,112
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 149.98
Params size (MB): 96.18
Estimated Total Size (MB): 246.74
----------------------------------------------------------------
3、模型训练
1、构建训练集
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 总数目
num_batch = len(dataloader) # 批次数目
train_acc, train_loss = 0, 0
for X, y in dataloader:
X, y = X.to(device), y.to(device)
predict = model(X)
loss = loss_fn(predict, y)
# 梯度清0、求导、重新设置参数
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_acc += (predict.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batch
return train_acc, train_loss
2、构建测试集
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batch = len(dataloader)
test_acc, test_loss = 0, 0
with torch.no_grad():
for X, y in dataloader:
X, y = X.to(device), y.to(device)
predict = model(X)
loss = loss_fn(predict, y)
test_acc += (predict.argmax(1) == y).type(torch.float).sum().item()
test_loss += loss.item()
test_acc /= size
test_loss /= num_batch
return test_acc, test_loss
3、设置超参数
learn_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learn_rate)
loss_fn = nn.CrossEntropyLoss()
4、模型正式训练
import copy
epochs = 60
train_acc, train_loss, test_acc, test_loss = [], [], [], []
best_acc = 0
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
# 保存最佳模型到 best_model
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_model = copy.deepcopy(model)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 获取当前的学习率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH = './best_model.pth' # 保存的参数文件名
torch.save(model.state_dict(), PATH)
print('Done')
Epoch: 1, Train_acc:59.0%, Train_loss:1.106, Test_acc:71.1%, Test_loss:0.740, Lr:1.00E-04
Epoch: 2, Train_acc:70.0%, Train_loss:0.776, Test_acc:83.1%, Test_loss:0.468, Lr:1.00E-04
Epoch: 3, Train_acc:75.2%, Train_loss:0.661, Test_acc:84.0%, Test_loss:0.461, Lr:1.00E-04
Epoch: 4, Train_acc:77.4%, Train_loss:0.605, Test_acc:88.4%, Test_loss:0.396, Lr:1.00E-04
Epoch: 5, Train_acc:80.3%, Train_loss:0.529, Test_acc:82.7%, Test_loss:0.415, Lr:1.00E-04
Epoch: 6, Train_acc:83.8%, Train_loss:0.422, Test_acc:83.6%, Test_loss:0.416, Lr:1.00E-04
Epoch: 7, Train_acc:85.8%, Train_loss:0.423, Test_acc:87.1%, Test_loss:0.343, Lr:1.00E-04
Epoch: 8, Train_acc:85.6%, Train_loss:0.393, Test_acc:87.6%, Test_loss:0.306, Lr:1.00E-04
Epoch: 9, Train_acc:86.3%, Train_loss:0.354, Test_acc:89.3%, Test_loss:0.338, Lr:1.00E-04
Epoch:10, Train_acc:86.6%, Train_loss:0.340, Test_acc:92.9%, Test_loss:0.276, Lr:1.00E-04
Epoch:11, Train_acc:88.8%, Train_loss:0.317, Test_acc:90.2%, Test_loss:0.290, Lr:1.00E-04
Epoch:12, Train_acc:87.9%, Train_loss:0.327, Test_acc:88.0%, Test_loss:0.338, Lr:1.00E-04
Epoch:13, Train_acc:89.2%, Train_loss:0.315, Test_acc:92.0%, Test_loss:0.337, Lr:1.00E-04
Epoch:14, Train_acc:91.1%, Train_loss:0.230, Test_acc:92.0%, Test_loss:0.369, Lr:1.00E-04
Epoch:15, Train_acc:93.3%, Train_loss:0.182, Test_acc:89.8%, Test_loss:0.278, Lr:1.00E-04
Epoch:16, Train_acc:91.6%, Train_loss:0.229, Test_acc:90.2%, Test_loss:0.290, Lr:1.00E-04
Epoch:17, Train_acc:90.9%, Train_loss:0.230, Test_acc:91.6%, Test_loss:0.272, Lr:1.00E-04
Epoch:18, Train_acc:93.9%, Train_loss:0.152, Test_acc:92.0%, Test_loss:0.280, Lr:1.00E-04
Epoch:19, Train_acc:94.7%, Train_loss:0.159, Test_acc:92.0%, Test_loss:0.262, Lr:1.00E-04
Epoch:20, Train_acc:95.9%, Train_loss:0.124, Test_acc:91.1%, Test_loss:0.260, Lr:1.00E-04
Epoch:21, Train_acc:95.7%, Train_loss:0.102, Test_acc:88.9%, Test_loss:0.342, Lr:1.00E-04
Epoch:22, Train_acc:95.9%, Train_loss:0.113, Test_acc:92.4%, Test_loss:0.275, Lr:1.00E-04
Epoch:23, Train_acc:96.1%, Train_loss:0.130, Test_acc:92.9%, Test_loss:0.308, Lr:1.00E-04
Epoch:24, Train_acc:94.8%, Train_loss:0.161, Test_acc:86.7%, Test_loss:0.456, Lr:1.00E-04
Epoch:25, Train_acc:95.2%, Train_loss:0.139, Test_acc:89.3%, Test_loss:0.428, Lr:1.00E-04
Epoch:26, Train_acc:96.0%, Train_loss:0.103, Test_acc:92.9%, Test_loss:0.313, Lr:1.00E-04
Epoch:27, Train_acc:96.0%, Train_loss:0.098, Test_acc:88.9%, Test_loss:0.520, Lr:1.00E-04
Epoch:28, Train_acc:97.2%, Train_loss:0.079, Test_acc:91.1%, Test_loss:0.404, Lr:1.00E-04
Epoch:29, Train_acc:98.6%, Train_loss:0.037, Test_acc:92.0%, Test_loss:0.270, Lr:1.00E-04
Epoch:30, Train_acc:98.8%, Train_loss:0.033, Test_acc:88.4%, Test_loss:0.520, Lr:1.00E-04
Epoch:31, Train_acc:95.6%, Train_loss:0.139, Test_acc:91.6%, Test_loss:0.370, Lr:1.00E-04
Epoch:32, Train_acc:96.7%, Train_loss:0.116, Test_acc:89.3%, Test_loss:0.376, Lr:1.00E-04
Epoch:33, Train_acc:96.4%, Train_loss:0.102, Test_acc:91.6%, Test_loss:0.342, Lr:1.00E-04
Epoch:34, Train_acc:98.6%, Train_loss:0.049, Test_acc:87.1%, Test_loss:0.417, Lr:1.00E-04
Epoch:35, Train_acc:97.9%, Train_loss:0.068, Test_acc:90.7%, Test_loss:0.423, Lr:1.00E-04
Epoch:36, Train_acc:98.3%, Train_loss:0.048, Test_acc:89.3%, Test_loss:0.492, Lr:1.00E-04
Epoch:37, Train_acc:98.0%, Train_loss:0.054, Test_acc:91.1%, Test_loss:0.355, Lr:1.00E-04
Epoch:38, Train_acc:98.6%, Train_loss:0.060, Test_acc:92.4%, Test_loss:0.402, Lr:1.00E-04
Epoch:39, Train_acc:97.9%, Train_loss:0.065, Test_acc:86.7%, Test_loss:0.498, Lr:1.00E-04
Epoch:40, Train_acc:98.0%, Train_loss:0.055, Test_acc:88.4%, Test_loss:0.514, Lr:1.00E-04
Epoch:41, Train_acc:99.1%, Train_loss:0.029, Test_acc:90.7%, Test_loss:0.381, Lr:1.00E-04
Epoch:42, Train_acc:98.0%, Train_loss:0.069, Test_acc:92.4%, Test_loss:0.377, Lr:1.00E-04
Epoch:43, Train_acc:99.4%, Train_loss:0.021, Test_acc:90.2%, Test_loss:0.403, Lr:1.00E-04
Epoch:44, Train_acc:98.0%, Train_loss:0.055, Test_acc:85.3%, Test_loss:0.686, Lr:1.00E-04
Epoch:45, Train_acc:98.0%, Train_loss:0.074, Test_acc:91.1%, Test_loss:0.321, Lr:1.00E-04
Epoch:46, Train_acc:98.6%, Train_loss:0.038, Test_acc:91.6%, Test_loss:0.426, Lr:1.00E-04
Epoch:47, Train_acc:97.4%, Train_loss:0.075, Test_acc:87.1%, Test_loss:0.604, Lr:1.00E-04
Epoch:48, Train_acc:99.6%, Train_loss:0.027, Test_acc:91.6%, Test_loss:0.379, Lr:1.00E-04
Epoch:49, Train_acc:99.8%, Train_loss:0.007, Test_acc:92.4%, Test_loss:0.381, Lr:1.00E-04
Epoch:50, Train_acc:100.0%, Train_loss:0.007, Test_acc:92.9%, Test_loss:0.361, Lr:1.00E-04
Epoch:51, Train_acc:99.8%, Train_loss:0.018, Test_acc:90.7%, Test_loss:0.446, Lr:1.00E-04
Epoch:52, Train_acc:99.0%, Train_loss:0.032, Test_acc:89.8%, Test_loss:0.588, Lr:1.00E-04
Epoch:53, Train_acc:97.7%, Train_loss:0.060, Test_acc:90.7%, Test_loss:0.456, Lr:1.00E-04
Epoch:54, Train_acc:97.7%, Train_loss:0.059, Test_acc:89.8%, Test_loss:0.506, Lr:1.00E-04
Epoch:55, Train_acc:98.6%, Train_loss:0.046, Test_acc:90.7%, Test_loss:0.350, Lr:1.00E-04
Epoch:56, Train_acc:99.7%, Train_loss:0.010, Test_acc:91.6%, Test_loss:0.349, Lr:1.00E-04
Epoch:57, Train_acc:99.6%, Train_loss:0.012, Test_acc:91.6%, Test_loss:0.369, Lr:1.00E-04
Epoch:58, Train_acc:98.9%, Train_loss:0.053, Test_acc:88.9%, Test_loss:0.666, Lr:1.00E-04
Epoch:59, Train_acc:98.2%, Train_loss:0.054, Test_acc:87.1%, Test_loss:0.509, Lr:1.00E-04
Epoch:60, Train_acc:98.7%, Train_loss:0.037, Test_acc:90.2%, Test_loss:0.513, Lr:1.00E-04
Done
5、结果显示和评估
1、结果显示
import matplotlib.pyplot as plt
#隐藏警告和显示中文
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 #分辨率
x = range(epochs)
# 创建画板
plt.figure(figsize=(12, 3))
# 子图一
plt.subplot(1, 2, 1)
plt.plot(x, train_acc, label='Train Accurary')
plt.plot(x, test_acc, label='Test Accurary')
plt.legend(loc='lower right')
plt.title("Train and test Accurary")
# 子图二
plt.subplot(1, 2, 2)
plt.plot(x, train_loss, label='Train loss')
plt.plot(x, test_loss, label='Test loss')
plt.legend(loc='upper right')
plt.title("Train and test Loss")
plt.show()
👀 解释：
- 总体效果还是不错的，损失率低于1，但是测试集的损失率有点小小不稳定；
- 准确率：刚开始出现了欠拟合的现象，但是后面好了，训练准确率稳定在100%附件(98%、99%等)，测试集稳定在90%附件；
- 整体：yolov5这个用于目标检测的网络用语目标识别也是有不错的效果。
2、评估
best_model.load_state_dict(torch.load(PATH, map_location=device))
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
epoch_test_acc, epoch_test_loss
(0.9022222222222223, 0.5129974257313852)
- 准确率在0.9左右，效果良好，且损失率为0.5，低于1.0。
